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Zhipu AI's GLM-5 Becomes the First Frontier Model Trained Entirely on Chinese Chips, Rivaling Western Labs Under an MIT License

China's Zhipu AI releases a 744-billion-parameter open-source model trained on Huawei Ascend chips that matches Claude and GPT on key benchmarks, sending its stock up 34 percent.

AI & Machine Learning Models
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Overview

Zhipu AI, the Tsinghua University spinoff that listed on the Hong Kong Stock Exchange in January at a $6.7 billion valuation according to CNBC, released GLM-5 on February 11 — a 744-billion-parameter open-source model that the company says is the first frontier-scale AI system trained entirely on non-NVIDIA hardware. The model, available under the MIT license, ranks first among open-source models on multiple industry benchmarks and competes directly with closed-source offerings from Anthropic and OpenAI, according to results reported by TechBuddies and BuildFastWithAI.

Zhipu AI’s shares surged 34 percent on the Hong Kong exchange following the announcement, according to Techloy.

Architecture and Training

GLM-5 uses a Mixture-of-Experts (MoE) architecture with 744 billion total parameters, of which 40 billion are active per token. The model employs 256 expert modules with 8 activated per inference step, roughly doubling the scale of its predecessor GLM-4.5’s 355 billion parameters, according to DigitalApplied. Pre-training consumed 28.5 trillion tokens, as reported by TechBuddies.

The model supports a 200,000-token context window using DeepSeek Sparse Attention (DSA) and can produce outputs of up to 131,000 tokens, according to DigitalApplied.

Perhaps the most geopolitically significant detail: GLM-5 was trained entirely on Huawei Ascend chips using the MindSpore framework, according to WinBuzzer and DigitalApplied. If confirmed independently, this would make it the first frontier-scale MoE model built without access to U.S.-manufactured semiconductor hardware — a practical demonstration of China’s AI compute independence strategy under ongoing export controls.

Benchmark Performance

GLM-5 outperforms Anthropic’s Claude Opus 4.5 on several benchmarks while trailing on others, according to data compiled by BuildFastWithAI:

  • BrowseComp (web browsing): 62.0 base, or 75.9 with context management, vs. Claude Opus 4.5’s 37.0 — the highest score among all tested models
  • Humanity’s Last Exam (with tools): 50.4 vs. Claude’s 43.4
  • SWE-bench Verified (software engineering): 77.8 vs. Claude’s 80.9
  • AIME 2026 I (mathematics): 92.7 vs. Claude’s 93.3
  • GPQA-Diamond (graduate-level science): 86.0 vs. Claude’s 87.0

The model also surpasses OpenAI’s GPT-5.2 on BrowseComp with context management (75.9 vs. 65.8) and Terminal-Bench 2.0 (56.2 vs. 54.0), though Claude Opus 4.5 leads the same Terminal-Bench 2.0 benchmark with a score of 59.3. GPT-5.2 leads on GPQA-Diamond (92.4 vs. 86.0).

On hallucination resistance, GLM-5 scored −1 on the AA-Omniscience Index, a 35-point improvement over GLM-4.5. Artificial Analysis described it as “the best hallucination profile in the industry,” according to TechBuddies.

Pricing and Access

GLM-5 is available as open weights on Hugging Face and ModelScope under the MIT license, the most permissive standard license in AI. It can be accessed for free through Z.ai’s chat interface, via API at api.z.ai, and through OpenRouter.

The pricing undercuts closed-source competitors significantly. API access runs approximately $0.80–$1.00 per million input tokens and $2.56–$3.20 per million output tokens, according to TechBuddies — roughly 6 to 10 times cheaper than Claude Opus 4.6’s $5.00/$25.00 per million tokens.

However, Zhipu AI simultaneously raised prices on its GLM Coding Plan by 30 percent and removed first-purchase discounts, citing compute constraints. “Compute is very tight. Even before the GLM-5 launch, we were pushing every chip to its limit,” the company said, as reported by Techloy. The model requires 1,490 GB of memory — double GLM-4.5’s footprint.

Post-Training Innovation

Zhipu AI introduced a novel reinforcement learning framework called “Slime,” an asynchronous system designed to address the generation bottleneck that typically consumes over 90 percent of RL training time. The framework uses a technique called Active Partial Rollouts (APRIL) and a three-part modular system integrating Megatron-LM for training, SGLang with custom routers for rollout, and a centralized Data Buffer, according to TechBuddies.

Safety Concerns

Not all observers are convinced the model’s capabilities come without risk. Lukas Petersson of Andon Labs noted that GLM-5 “achieves goals via aggressive tactics but doesn’t reason about its situation,” raising concerns about optimization without situational awareness that parallel the “paperclip maximizer” thought experiment, according to TechBuddies.

What We Don’t Know

Several questions remain unanswered. The claim that GLM-5 was trained entirely on Huawei Ascend chips has not been independently verified. The model’s safety evaluation details beyond hallucination metrics have not been published. And while benchmark results are strong, the gap between controlled evaluations and real-world deployment performance remains unclear — particularly for the model’s agentic capabilities, which include generating formatted documents and executing multi-step workflows.

Zhipu AI, founded in 2019, has raised approximately $1.5 billion to date from investors including Alibaba, Tencent, Meituan, Xiaomi, and Saudi Aramco’s Prosperity7 Ventures.